Using supervised learning to classify metadata of research data by field of study

Abstract

Many interesting use cases of research data classifiers presuppose that a research data item can be mapped to more than one field of study, but for such classification mechanisms, reproducible evaluations are lacking. This paper closes this gap: It describes the creation of a training and evaluation set comprised of labeled metadata, evaluates several supervised classification approaches, and comments on their application in scientometric research. The metadata were retrieved from the DataCite index of research data, pre processed, and compiled into a set of 613,585 records. According to our experiments with 20 general fields of study, multi layer perceptron models perform best, followed by long short-term memory models. The models can be used in scientometric research, for example to analyze interdisciplinary trends of digital scholarly output or to characterize growth patterns of research data, stratified by field of study. Our findings allow us to estimate errors in applying the models. The best performing models and the data used for their training are available for re use.